Name
Abstract | ||
|---|---|---|
In recent years, with the further adoption of the Internet of Things and sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of traffic sensor data have had rapid development. Traffic sensor data gathered by large amounts of sensors show some new features, such as massiveness, continuity, streaming, and spatio-temporality. ITS applications utilizing traffic sensor data can be divided into three main types: 1) offline processing of historical data; 2) online processing of streaming data; and 3) hybrid processing of both. Current research tends to solve these problems in separate solutions, such as stream computing and batch processing. In this paper, we propose a hybrid processing approach and present corresponding system implementation for both streaming and historical traffic sensor data, which combines spatio-temporal data partitioning, pipelined parallel processing, and stream computing techniques to support hybrid processing of traffic sensor data in real-time. Three types of real-world applications are explained in detail to show the usability and generality of our approach and system. Our experiments show that the system can achieve better performance than a popular open-source streaming system called Storm. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ACCESS.2015.2500258 | IEEE ACCESS |
Keywords | Field | DocType |
Traffic sensor data,spatio-temporal data object,real-time processing,stream computing | Data modeling,Data stream mining,Data processing,Computer science,Stream,Usability,Computer network,Real-time computing,Batch processing,Intelligent transportation system,Distributed database,Distributed computing | Journal |
Volume | ISSN | Citations |
3 | 2169-3536 | 4 |
PageRank | References | Authors |
0.44 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuofeng Zhao | 1 | 66 | 15.46 |
| Weilong Ding | 2 | 9 | 5.09 |
| Jianwu Wang | 3 | 215 | 26.72 |
| Yanbo Han | 4 | 500 | 59.74 |